statistical_test

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description mentions returning numerical results and a visualization, but lacks details on side effects (e.g., whether data is modified), required permissions, or assumptions (e.g., dataset must be loaded). With no annotations, the description provides moderate transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the main action, no fluff. Efficient and to the point.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (6 parameters, many test options) and existence of an output schema, the description is minimally complete. It does not explain how to use parameters like group_column vs column_y, or what the diagnostic plot shows. More context would benefit a complex tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description does not add significant meaning beyond the schema; it reiterates test types already in test_type description and does not clarify parameter combinations or defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool runs a statistical test and returns results with a diagnostic plot. It lists supported test types (t-test, correlation, etc.), making the purpose specific and distinct from general analysis tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus sibling tools like analyze_data or auto_eda. No mention of prerequisites, limitations, or alternative tools for specific tasks.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.